Feature Selection for Personalized Policy Analysis

TL;DR

This paper introduces Forest-PLS, a novel feature selection method combining partial least squares and causal forests to analyze policy effect heterogeneity across subgroups, demonstrated on reemployment data.

Contribution

The paper presents Forest-PLS, a new approach that captures complex heterogeneity in policy effects using a combination of PLS and causal forests, improving analysis flexibility.

Findings

Financial incentives motivate some young non-white individuals to enter the labor market.

Incentives can also temporarily dissuade others from seeking employment.

Targeted, personalized measures are necessary for effective policy interventions.

Abstract

In this paper, we propose Forest-PLS, a feature selection method for analyzing policy effect heterogeneity in a more flexible and comprehensive manner than is typically available with conventional methods. In particular, our method is able to capture policy effect heterogeneity both within and across subgroups of the population defined by observable characteristics. To achieve this, we employ partial least squares to identify target components of the population and causal forests to estimate personalized policy effects across these components. We show that the method is consistent and leads to asymptotically normally distributed policy effects. To demonstrate the efficacy of our approach, we apply it to the data from the Pennsylvania Reemployment Bonus Experiments, which were conducted in 1988-1989. The analysis reveals that financial incentives can motivate some young non-white individuals to enter the labor market. However, these incentives may also provide a temporary financial cushion for others, dissuading them from actively seeking employment. Our findings highlight the need for targeted, personalized measures for young non-white male participants.